Evidence map›Paper›PMID 40794987›Full record

ArticleThe western journal of emergency medicine2025

Low Frequency, High Complexity: Assessing Skill Decay in Transesophageal Echocardiography Post-Simulation Training.

Enyo Ablordeppey, Emily Terian, Collyn T Murray, Laura Wallace, Wendy Huang, Erica Blustein, Alexander Croft, Ernesto Romo, Mansi Agarwal, Daniel Theodoro

Abstract read
In one paragraph

Article in The western journal of emergency medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Enyo AblordeppeyWashington University School of Medicine, Department of Anesthesiology, St. Louis, Missouri.
Emily TerianWashington University School of Medicine, St. Louis, Missouri. University of Chicago, Department of Emergency Medicine, Chicago, Illinois.
Collyn T MurrayWashington University School of Medicine, Department of Emergency Medicine, St. Louis, Missouri.
Laura WallaceWashington University School of Medicine, Department of Emergency Medicine, St. Louis, Missouri.
Wendy HuangWashington University School of Medicine, Department of Emergency Medicine, St. Louis, Missouri.
Erica BlusteinWashington University School of Medicine, Department of Emergency Medicine, St. Louis, Missouri.
Alexander CroftWashington University School of Medicine, Department of Emergency Medicine, St. Louis, Missouri.
Ernesto RomoWashington University School of Medicine, Department of Emergency Medicine, St. Louis, Missouri.
Mansi AgarwalWashington University School of Medicine, Department of Biostatistics, St. Louis, Missouri.
Daniel TheodoroWashington University School of Medicine, Department of Emergency Medicine, St. Louis, Missouri.

Funding

WU INSTITUTE OF CLINICAL AND TRANSLATIONAL SCIENCESUL1TR002345 · NCATS · WASHINGTON UNIVERSITY · PI William G. Powderly · 2017 to 2026
$97.8M
Adapting and testing a deimplementation program in the Intensive Care UnitK01HL161026 · NHLBI · WASHINGTON UNIVERSITY · PI ABLORDEPPEY, ENYO · 2022 to 2024
$522k
NCATS NIH HHS UL1 TR002345NHLBI NIH HHS K01 HL161026
6 · The paper itself

Abstract

introductionResuscitative transesophageal echo (rTEE) is a promising adjunct to cardiac arrest resuscitation. However, it is a high-acuity diagnostic tool that is rarely used in this setting and its safety establishment is limited because of low occurrence. High-acuity, low occurrence skills such as rTEE during cardiac arrest inevitably decay. In this study we examined the content and percentage of rTEE skill decay following simulation-based education (SBE).

methodsResuscitative TEE-naïve emergency physicians (EP) were trained using a combination of clinical exposure, web-based didactics, and monthly hands-on sessions with a high-fidelity rTEE simulator for four months. The COVID-19 pandemic created a natural wash-out phase where EPs did not perform any actual or SBE for six months after initial training. Unadvertised assessment of rTEE skill occurred at month 6 after rTEE training to test skill decay and at month 7 to determine the effect of spaced repetition. One year later, the EPs completed a questionnaire assessing rTEE comfort. Statistical measures were used to measure skill decay.

resultsSeven EPs were individually evaluated in four domains: name recall; probe manipulation (rotation); probe manipulation (omniplane); and image acquisition adequacy. At the end of training, all participants reached a full proficiency score of 32. At month 6, the mean score was 19 of 32 (SD ±7), reflecting a 41% decay (95% confidence interval (CI) -54%, -27%; P < .001) for eight standard rTEE views. Following spaced repetition at month 7, the median score improved to 26 (IQR 23-30), representing a 19% decay (95% CI -35%, -4%; P < .02). For the three guideline-recommended views, the overall decay percentage was 26% (95% CI -36%, -16%; P < .001), although image acquisition skills did not show significant decay. Spaced repetition resulted in a 23% increase in mean scores (95% CI 9-37%), and the average time to obtain all eight rTEE views decreased from 7.3 minutes at month 6 to 5.7 minutes at month 7.

conclusionAfter focused, proficiency-based SBE in rTEE, hands-on image acquisition skills showed the least decay compared to name recall and probe manipulation. Spaced repetition mitigated decay over one month, although not back to baseline.

Indexed as

Cardiopulmonary ResuscitationClinical CompetenceEchocardiography, TransesophagealEmergency MedicineHeart ArrestSimulation TrainingCOVID-19Educational MeasurementFemaleHumansMaleSARS-CoV-2

Identifiers

PMID40794987
PMCPMC12342464

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.